{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.1　绘制折线图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  2.1.1　使用plot()绘制折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x3d68280>,\n",
       " <matplotlib.lines.Line2D at 0x3d68340>,\n",
       " <matplotlib.lines.Line2D at 0x3d683d0>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])\n",
    "plt.plot(arr[0], arr[1:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1.2　实例1：未来15天最高气温和最低气温"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZVewOVn+xjtvg8faPO/nHNz/z01NX0OTSKj49tyV6Y4opd8ct4JMOVn/J2XGbmaW8P+oy+rWOcjssg/OGPPCVRVxSKYJZD/T2+fkt0RtTQqrKyt3HiAgXujYO/NEuR86kcsfkFRw7m8a8x/qWapOSyduGxJNc/8YS/npzR37To4nPz19Qog+s2xFjApSI0LP5pXRr4rtRNP5Uq2oF3rijK2dSM3jyk3VW1TMAzEpIpHx4GEM7+b/kQW6W6I0JUa3rRPLs0A4s3n6EdxbtcjucMi09M4sv1x3g6va1SzxXozgs0RsTwkZ2b8R1nerx8rdbSdh33O1wyqxF21I4ejatVCpV5sUSvTEhTET427BO1K1WkUc/XsPJ84FbtjmUzU5IomaV8vRv407HuCV6Y0JctUoRvDayC8knL/CH2Rus5n0pO3kunQVbDnFDdH0iXBqlZYnemDKga+Ma/M/ANny1IZnpq/e7HU6Z8tWGZNIyshjWNc+lOEqFJXpjyohx/ZrTt1Ut/vTFJrYdOu12OGXG7IREWtauSqcG7qyDAJbojSkzwsKECcNjiKwYwcMfJXA+zdbV9be9R88St/c4w7o2cHVYriV6Y8qQqMgKTBgezbZDZ3h+7ma3wwl5c9YkIQI3xbjXbAOW6I0pc/q1jmJ8/xZ8vGofX61PdjuckKWqzE5IoneLS6lfvZKrsRSa6EWkkYgsFJHNIrJJRB7zbJ8hIms9X3tEZG0+x+8RkQ2e/ayugTEB4MmBrYlpVJ2nZ69n/7FzbocTkuL3HmffsXOujZ3PyZs7+gzgSVVtD/QEHhKR9qp6u6rGqGoMMAuYXcA5Bnj2zbMOgzGmdEWEh/H6yC6g8MjHa0jPzHI7pJAzKyGJShHhDOpY1+1QCk/0qpqsqgmex6eBLcDFBidxehiGAx/7K0hjjO81qlmZF27pzNr9J5iwYJvb4YSUC+mZzF1/gEEd61KlgnslrLMVqY1eRJoCXYCVOTb3BQ6p6vZ8DlPgWxGJF5GxBZx7rIjEiUhcSkpKUcIyxhTTdZ3rMbJ7Y97+cSeLttnfna98v+Uwpy9kuDp2PievE72IVMVponlcVU/leGkkBd/NX66qXYHBOM0+/fLaSVUnqWqsqsZGRVn9bGNKy7ND29O6TlV++8k6Uk6nuh1OSJidkEidSyrQu0Utt0MBvEz0IhKBk+SnqersHNvLAcOAGfkdq6pJnu+HgTlA95IEbIzxrUrlw3l9ZFdOX0jnt5+stZLGJXTkTCo/bUvhpi4NAmbRdm9G3QjwHrBFVSfkevlq4GdVTczn2CoiEpn9GBgIbCxZyMYYX2tTN5LnrndKGk9abCWNS+LLdQfIyNKAGG2TzZs7+j7AXcCVOYZTDvG8NoJczTYiUl9E5nme1gGWiMg6YBXwlap+46PYjTE+lF3S+KX5VtK4JGYnJNGxwSW0qRvpdigXFdodrKpLgDw/f6jqqDy2HQCGeB7vAqJLFqIxpjRklzReu/8Ej368hq8e7Uu1SqW/SEYw23boNBuSTvLs0PZuh/ILNjPWGHNRtUoRvH6HlTQurtkJSYSHCTfElP5ygQWxRG+M+YWujWvw5MDWVtK4iDKzlM/WJNG/dRS1qlZwO5xfsERvjPmV8f1acHlLK2lcFMt3HuXgqQsBM3Y+J0v0xphfCQsTJtweTWTFcjz8UQIX0q2kcWFmr0kksmI5rm5Xx+1QfsUSvTEmT7UjKzJheIyVNPbC2dQMvtl4kKGd61ExItztcH7FEr0xJl/9Wkcxrn9zPlppJY0LMn/TQc6lZTKsa+CMnc/JEr0xpkD/M7CNlTQuxOyEJBrVrERskxpuh5InS/TGmALlLGn86HQraZxb8snzLN15hGFdGrq6XGBBLNEbYwrVqGZl/n5LJ9bsO8HL31pJ45w+W3MAVbi5S+CNtslmid4Y45WhneszsnsjJv60k8XbraQxZC8XmEi3JjVoWquK2+HkyxK9McZrzw7tQKvaVXlihpU0Bth04BTbD58JyLHzOVmiN8Z4rVL5cN64w0oaZ5uVkEj58DCGdgqskge5WaI3xhRJm7qRPHt9+zJf0jg9M4sv1h7g6va1qVY5sIu/WaI3xhTZHd0bM6RTXV6av5U1ZbSk8aJtKRw9mxZQdefzY4neGFNkIsLfh3WmziUVeeTjNZw8n+52SKVudkISNauUp3+bwF/61BK9MaZYqlWK4LWRZbOk8clz6SzYcogbousTER74adSbpQQbichCEdksIptE5DHP9j+JSFIeq07lPn6QiGwVkR0i8rSvfwBjjHu6NSmbJY2/2pBMWkZWwI+2yebNW1EG8KSqtgd6Ag+JSPbyKa+oaozna17uA0UkHHgTGAy0B0bmONYYEwKySxr/+cuyU9J4zppEWtauSqcG1dwOxSuFJnpVTVbVBM/j08AWwNu3se7ADlXdpappwHTgxuIGa4wJPNkljatWKBsljfcePcvqPccZ1rVBwJY8yK1IjUsi0hToAqz0bHpYRNaLyBQRyauaTwMg5+e5RPJ5kxCRsSISJyJxKSk2686YYFKWShq/tXAnEeES0CUPcvM60YtIVWAW8LiqngLeBloAMUAy8HJJAlHVSaoaq6qxUVGB34ttjPmlnCWN520IzZLGWw+e5tP4/dzdqyn1qlVyOxyveZXoRSQCJ8lPU9XZAKp6SFUzVTULmIzTTJNbEtAox/OGnm3GmBCUXdL497NCs6Tx37/eQpUK5Xh4QEu3QykSb0bdCPAesEVVJ+TYXi/HbjcDG/M4fDXQSkSaiUh5YATwRclCNsYEqlAuabx0xxF+3JrCwwNaUqNKebfDKRJv7uj7AHcBV+YaSvlPEdkgIuuBAcATACJSX0TmAahqBvAwMB+nE/cTVd3kjx/EGBMYcpY0fmVBaJQ0zspS/jZvCw2qV+Ke3k3dDqfIyhW2g6ouAfLqWv7VcErP/geAITmez8tvX2NMaBrauT5Ldxzh7Z920qvFpfRtFdz9bp+vS2LTgVP86/aYgFwTtjCBP6XLGBOUnh3agZZRwV/S+EJ6Ji/N30bHBpdwQ3RgV6nMjyV6Y4xfhEpJ46nL9pB04jx/GNyOsLDgGDefmyV6Y4zf5CxpPDkISxofP5vGmwt3MKBNFL1b1nI7nGKzRG+M8avsksYvBmFJ49d/2MHZ1AyeGdLO7VBKxBK9Mcavcpc0PnUhOEoa7z16ln+v2MPw2Ea0rhPpdjglYoneGON3OUsaPxMkJY3/OX8r5cLCeOKa1m6HUmKW6I0xpeJiSeP1ycwI8JLGa/Yd56v1ydzftxl1LqnodjglZoneGFNqsksa/+nLTWwP0JLGqsrf5/1MrarlGdu/hdvh+IQlemNMqfllSeM1AVnSeMHmQ6zac4zHr25N1QqFzikNCpbojTGlKruk8dZDp/lLgJU0Ts/M4oVvfqZ5VBVGXNao8AOChCV6Y0ypyy5pPC3AShpPX72fXSlneXpQW8oFwVqw3gqdn8QYE1QCraTxmdQMXv1uG92b1uSa9nXcDsenLNEbY1yRs6TxYwFQ0njSTzs5ciaNP1zXLmiWCPSWJXpjjGuySxon7DvBX+Zudm18/aFTF5i8eDdDO9cjplF1V2Lwp9DoUjbGBK2hneuzdt8J3l2ym7OpmbxwSyciSrl9fMK328jIyuJ317Yt1euWFkv0xhjX/fG6dlStWI5/fbed4+fSePOOrlQqXzp137PXgR3VuxmNL61cKtcsbd4sJdhIRBaKyGYR2SQij3m2vygiP4vIehGZIyLV8zl+j2clqrUiEufj+I0xIUBEePzq1vzfTR1ZuPUwd763khPn0krl2i941oF95MrgWge2KLz5fJQBPKmq7YGewEMi0h5YAHRU1c7ANuCZAs4xQFVjVDW2xBEbY0LWnT2b8NYdXdmQeJLbJi4n+eR5v15v2Y4jLAzSdWCLotBEr6rJqprgeXwaZ+3XBqr6rWdNWIAVQEP/hWmMKSsGd6rH1NGXkXzyAre8tYwdh/1TKiErS/lrEK8DWxRF6vEQkaZAF2BlrpdGA1/nc5gC34pIvIiMLeDcY0UkTkTiUlJSihKWMSbE9G5Ri+lje5KWqdw6cblf6thnrwP71LVtgnId2KLwOtGLSFVgFvC4qp7Ksf2POM070/I59HJV7QoMxmn26ZfXTqo6SVVjVTU2Kiq4FxI2xpRcxwbVmPVAL6pViuCOyStZuPWwz84dCuvAFoVXiV5EInCS/DRVnZ1j+yhgKPAbzWcArKomeb4fBuYA3UsYszGmjGhyaRVmju9N86gq3P9BHHPWJPrkvB+EwDqwReHNqBsB3gO2qOqEHNsHAb8DblDVPOcvi0gVEYnMfgwMBDb6InBjTNkQFVmB6WN70r1ZTZ6YsY53S7j27PGzabwRAuvAFoU34+j7AHcBG0RkrWfbH4DXgArAAs904RWqOl5E6gPvquoQoA4wx/N6OeAjVf3Gtz+CuSj9PHzxKOz+yQ8nF2jWD3o/AvU6++H8PpK8Dpa9DueOwm9mQlhot72WFZEVI3j/3st4YsZa/u+rLaScTuXpwW2LVaogex3YpwcH9zqwRVFoolfVJUBev815+ex/ABjiebwLiC5JgMZL54/DxyNh3wrodBuU9/HEj/Tz8PNXsOETaH6Fk/BbXAWBUBNEFXZ8D8tec97kwiIgK9153OJKt6MzPlKhXDivj+xKzSobeWfRLo6cSSvyLNrsdWBv69aINnWDex3YorCZsaHgVDL8Zxgc2Q63ToGOw/xznfPHIX4qrJgI/7kFandwEn7HW6CcC2OQM9Jg40znDv7wZoisD9c8D9Ej4Y1YWDfdEn2ICQ8T/nJjR2pVrVCsWbQvetaB/e3A4F8HtiisqFmwO7Id3hsIJ/bBbz71X5IHqFQDLn8CHl8PN74FmgWfjYdXo2Hpq3DhpP+undP5E7DkFXi1M3z2ACBw00R4bB30eQyq1oYOw2DLl5AamMvVmeIr7izatftPMDeE1oEtCgnE1dhjY2M1Ls6qJRQqKR6m3QYI3DkT6ncp3eurwo7vPE0mi6B8JHS7B3o+ANX8MH/uxD7n00TCB5B2puAmpH0rYcpAuOltiLnD97GYgPD1hmQem76WJpdW5sP7ulOvWqU891NVbn9nBbuOnOHHpwaEzBKBOYlIfH7VB+yOPljt+B6mXg/lq8B935Z+kgcnuba6Bu75Esb+CK2vhRVvO3f4s+6H5PW+uU7yOpg1Bl6NgZUToc0QGLcY7v4cWl6ddz9Bo+5Qo5nTfGNClrezaENxHdiisDv6YLRhJswZD1Ft4M5ZEFnX7Yj+qyh33QXJ3cFavip0GwU9xkN1L9fy/PEf8OPf4YmN/vmEYQLGxqSTjHp/NRlZWbw/6jK6NK5x8bX0zCyu/dciAOY/3q/USyCXFrujDyUrJsKs+5w71nvnBVaSB6jeGAb9zUmuV/8JDv/sdNy+3QfWfux0oBYkIw3WfgRv94Zpt8CRbU4H6xOb4Nq/ep/kAToPBxTWf1KSn8gEgYJm0c7IsQ5sqCb5wtgdfbBQhR/+AotfhrZD4Zb3ICIIOpQyUp1PIMteh5QtzsiYnuOdu/OK1f673/kTzoielRPhdLLvRvRMGQTnjsFDKwNjKKjxq5TTqYx6fxVbD57mxds6c037ulzx4kKa16rKjHE9Q26JwJwKuqO3RB8MMjNg7uOw5t9OgrxuQvBNBMqv47bDMNg4q+RNPfmJnwpfPgb3L4QGXUt+PhPwTl9IZ9y/41m28yhdG1cnYd8JPnuoT0guEZiTJfpgln4eZo6GrfOg3+9gwB+C/870wBpY9gZsmgOaCRLu3Ln3fhjq+Xh+3fkT8FJr5w1yyD99e24TsFIzMnlixlrmbTjI0M71eOOO0H+Tt0QfrHLOdh38T+iRb5Xn4HRin9Ph2vLqorW9F9Wno5xPEb/92Z2JXcYVmVnKl+sO0L91VEgvKpLNOmOD0alkeH8IJMY5s11DLcmD03Ebe69/kzw4M2XPHXWajkyZER4m3NSlQZlI8oWxRB+IjuwovdmuZUGLK6FyLVhvY+pN2WSJPtAkxTszOtPPwai50GKA2xEFv/AIp9Db1q+d5jBjyhhL9IFk5w/uz3YNVdEjIDPN6QA2poyxRB8oNsyEacOhZjO4bwFc2sLtiEJLvWiIamclEUyZ5M0KU41EZKGIbBaRTSLymGd7TRFZICLbPd9r5HP8PZ59tovIPb7+AUJCoM92DQUizl39/pVwdKfb0RhTqry5o88AnlTV9kBPnAW+2wNPA9+raivge8/zXxCRmsBzQA+ctWKfy+8NoUxShe+fh29+78x2vXP2L2eLGt/qPBwQWD/D7UiMKVWFJnpVTVbVBM/j08AWoAFwI/CBZ7cPgJvyOPxaYIGqHlPV48ACYJAP4g5+mRnw5aNOSYNuo2D4h8FR0iCYXVLfmXm7brrzJmtMGVGkNnoRaQp0AVYCdVQ12fPSQZz1YXNrAOzP8TzRs63sSj8Pq9+DN7tDwofObNeh/wq+kgbBKnoEnNjrTEIzpozwujCziFQFZgGPq+qpnMWBVFVFpES3SCIyFhgL0Lhx45KcKjCdPQKr34VVk5zJO/W7wO3ToN1QtyMrW9oOhYgqsO5jaNLL7WiMKRVeJXoRicBJ8tNUdbZn8yERqaeqySJSDzicx6FJwBU5njcEfszrGqo6CZgETgkEr6IPBkd3wvI3Ye00yLgArQc7Rbua9A7+mjXBqEJVaH8DbPoMBv8DIvJekciYUOLNqBsB3gO2qOqEHC99AWSPorkH+DyPw+cDA0WkhqcTdqBnW+jbvwpm3Amvd3OqTnYeDg+tgjumQ9M+luTdFD0CUk86E6iMKQO8uaPvA9wFbBCRtZ5tfwBeAD4RkfuAvcBwABGJBcar6hhVPSYifwFWe457XlWP+fIHCChZmU7yWPaaM4yvYnXo+yR0HwuReXVhGFc07QuXNHA6Za28hCkDCk30qroEyO/286o89o8DxuR4PgWYUtwAg0L6eafNd9kbcGynU6xr8D+hy53OLFcTWMLCnU9YS1+DM4eham23IzLGr8reKrm+dPYorJ78yw7WW9+HdjdAuP1qA1rnEbDkFWdGcq8H3Y7GGL+ybFQcFztYP4KM89bBGoxqt4V6MU5FS0v0JsRZoi+K/auc9vctc52KiNEjoNfDENXG7chMcUSPdGYlH9oMddq7HY0xfhNaiX7PUtAs35/3zCFYNRn2r7AO1lDS8Rb49o/OXf01z7sdjTF+E1qJftqtTh13f7AO1tBTNQpaXgPrP4GrnrPZySZkhVai/81M/9zRl6sA9btaB2soih4B276G3T85K1EZE4JCK3M17eN2BCbYtB7kVAxdN90SvQlZtvCIKdsiKkKHm2HLl5B62u1ojPELS/TGRI90+na2fOl2JMb4hSV6Yxr1gBpNbZlBE7Is0Rsj4tzV714EJxPdjsYYn7NEbwxA59sBdYZaGhNiLNEbA1CzGTTuZcsMmpAUWsMrjSmJzrfD3MfhwBpo0LX0rrvrJzh1wPfnjagIrQbaBD9jid6YizrcBF//3rmrL41Erwrf/QmW/st/16hYHS4bAz3GWTnmMswSvTHZKtWANoNh40y49q9O4Tp/ycyALx+Dtf+B2NHQ+1HfX+NUEqx4Gxa/DMteh+jbodcjENXa99cyAa3QRC8iU4ChwGFV7ejZNgPILtlYHTihqjF5HLsHOA1kAhmqGuuTqI3xl+iRsPkz2PGdk/T9Ie0czLwXtn0DVzwD/X/vn/LWNZtB08vhyA5Y4SmrnfChldUug7zpjJ0KDMq5QVVvV9UYT3KfBczO47hsAzz7WpI3ga/lVVC5lrNimD+cPw7/vhm2zYfrXoYrnvZ/sq3VEoa+Ao9vhP5PO8tcTh0Ck6+ETXOcTxcmpBWa6FV1EZDnOq+ehcOHA376qzCmlIVHQKfbnLV/zx/37blPHYApg+FAAtw21Wk7L01Vo2DAM/DEJudN5sIJ+HQUvN4VVk6CtLOlG48pNSUdXtkXOKSq2/N5XYFvRSReRMYWdCIRGSsicSISl5KSUsKwjCmB6BGQmebc7fpKyjZ4b6AzIes3M52OX7eUr+y8yTwcB7f/B6rWga+fggnt4fu/OOvompBS0kQ/koLv5i9X1a7AYOAhEemX346qOklVY1U1NioqqoRhGVMC9aIhqq3vSiIkxsOUayHjAtz7FTTv75vzllRYOLS7HsYsgNHfOu35i1+GVzrCF484b04mJBQ70YtIOWAYMCO/fVQ1yfP9MDAH6F7c6xlTakScu/r9K531gUtix3fwwVCoeAmMnu+8iQSixj1gxDTnLr/Lb5wZwm9eBh+N8KzcZpPIgllJ7uivBn5W1TyLg4hIFRGJzH4MDAQ2luB6xpSeTsMBgfX53scUbv0n8NHtcGkL54750hY+C89vrOM2JBWa6EXkY2A50EZEEkXkPs9LI8jVbCMi9UVknudpHWCJiKwDVgFfqeo3vgvdGD+q1sBpYiluSYTlb8Hs+52yCqO+Cr71hQvquF37kdvRmSISDcCPZLGxsRoXF+d2GKasWzcd5oyDe7+BJr28O0YVvv8zLHkF2t0AwyY7pQiCXVYmbJ0Hiyc4o4ZGfAxth7gdlclBROLzG8ZuRc2MyU/boRBRxfsx9ZkZ8PnDTpKPHe0MoQyFJA//7bgd/Q3U7QyfPwgnk9yOynjJEr0x+alQ1Ulumz6D9PMF75t2Dmbc6ZQ06P80XDfBSY6hplwFuPV9yEhzmqayMt2OyHjBEr0xBYkeAaknnQlU+bk42/Ubpz17wDOhXVqgVksYOgH2LoVFL7odjfGCJXpjCtKsH0TWz39MvduzXd0SPQI6j4Cf/gF7lrgdjSmEJXpjChIWDp2HO+Phc88YDaTZrm647iWo0Qxm3Q/n8qySYgKEJXpjChM9AjQTNsz877acs11HzQ2c2a6lqUIk3DoFzh2Bzx60SVUBzBK9MYWp3Q7qxcB6T/PNju/gg+v/O9u1foyb0bmrfgxc8zxs+xpWvuN2NCYfluiN8Ub0SEheBwv/5sx2rdk8eGa7+luP8dB6ECz4f87vyAQcmzBljDfOpMDLbZwmnKZ9nbowFau5HVXgOHsUJl4OEZVg3E9Os04oUoXdP8GWLyEz3ffnrxDprG5WDAVNmLKlBI3xRtUo6PUgnD8BQ14KnYlQvlLlUrhlstOkNe8puHmi2xH5Vma6U+tn2WtwcAOUr+p8+VqVWr4/J5bojfHewP9zO4LA1vRy6Pc7+OkFaH6F04kd7FJPQ/wHztq7pxKhVmu44XWn6F0QvdlbojfG+E6/p2DPYpj7W2gQ60yuCkanDsDKiRA31Zkw1+RyZzJcq4EQFnxdm5bojTG+E17OKeQ2sY+zAPqY75yyCcHi0GZY9jps+NTpj2l/o7OQeoNubkdWIpbojTG+Va0B3PgWTB8JC56DwS+4HVHBsjtYl73uDJ2NqOwUpev1INRo6nZ0PmGJ3hjje22HOMMuV77tTCZrM9jtiH4tM90pWLfsNTi4HqrUhiv/n5PkK9d0OzqfskRvjPGPa56HvcucWbPjlzh3+oEg9TQkfOh0sJ7cH7QdrEXhzQpTU0TksIhszLHtTyKSJCJrPV95rkAgIoNEZKuI7BCRp30ZuDEmwF0saZwaGCWNTyU7TUkTOsD8P0D1JjByBjy4ErreHbJJHry7o58KvAF8mGv7K6r6Un4HiUg48CZwDZAIrBaRL1R1czFjNcYEm1otndEqn413Shpf4cL93qHNsPwNZw3f7A7WXo9Aw+DuYC2KQhO9qi4SkabFOHd3YIeq7gIQkenAjYAlemPKkpiRsOtHp6Rx077QtI//r6kKuxc57e85O1h7PgA1m/n/+gGmJG30D4vI3UAc8KSqHs/1egNgf47niUCP/E4mImOBsQCNGzcuQVjGmIBz3UuQuBpmjYEHlvqvszMzHTZ/7iT45HVQJQqu/F+IvS/kOliLorgj/98GWgAxQDLwckkDUdVJqhqrqrFRUVElPZ0xJpBklzQ+m+Kfksapp2H5m/BaF5h1n7P04/WvweMbnUlcZTjJQzHv6FX1UPZjEZkMzM1jtySgUY7nDT3bjDFlUf0YGPgX+OZpWDUJeowr+TlPJXtmsL7vmcHaB4a8CK2uDcoZrP5SrEQvIvVUNdnz9GZgYx67rQZaiUgznAQ/ArijWFEaY0JDj/FOe/23/wuNe0K96OKdxzpYi6TQRC8iHwNXALVEJBF4DrhCRGIABfYA4zz71gfeVdUhqpohIg8D84FwYIqqbvLHD2GMCRIizqzZiX3g03th3CKo4GUVSOtgLTarR2+MKX17ljgljTuPgJvfLnjfvGaw9hhb5jtYc7N69MaYwPKLksb98y5pnNcM1utfg863h/TkJn+wRG+McUfOksYNL/vvsozWwepzluiNMe7IXdL4+ldh1WTrYPUDS/TGGPfkLGk86QrrYPUTS/TGGHe1HeKsw5t6Crrdax2sfmCJ3hjjvu73ux1BSLOeDWOMCXGW6I0xJsRZojfGmBBnid4YY0KcJXpjjAlxluiNMSbEWaI3xpgQZ4neGGNCXECWKRaRFGBvMQ+vBRzxYTj+FEyxQnDFG0yxQnDFG0yxQnDFW5JYm6hqnuuwBmSiLwkRicuvJnOgCaZYIbjiDaZYIbjiDaZYIbji9Ves1nRjjDEhzhK9McaEuFBM9JPcDqAIgilWCK54gylWCK54gylWCK54/RJryLXRG2OM+aVQvKM3xhiTgyV6Y4wJcSGV6EUkXETWiMhct2MpjIhUF5GZIvKziGwRkV5ux5QfEXlCRDaJyEYR+VhEKrodU04iMkVEDovIxhzbaorIAhHZ7vlew80Ys+UT64ue/wfrRWSOiFR3McRfyCveHK89KSIqIrXciC23/GIVkUc8v99NIvJPt+LLLZ//CzEiskJE1opInIh098W1QirRA48BW9wOwkuvAt+oalsgmgCNW0QaAI8CsaraEQgHRrgb1a9MBQbl2vY08L2qtgK+9zwPBFP5dawLgI6q2hnYBjxT2kEVYCq/jhcRaQQMBPaVdkAFmEquWEVkAHAjEK2qHYCXXIgrP1P59e/2n8CfVTUGeNbzvMRCJtGLSEPgOuBdt2MpjIhUA/oB7wGoapqqnnA1qIKVAyqJSDmgMnDA5Xh+QVUXAcdybb4R+MDz+APgptKMKT95xaqq36pqhufpCqBhqQeWj3x+twCvAL8DAmY0Rz6xPgC8oKqpnn0Ol3pg+cgnXgUu8Tyuho/+1kIm0QP/wvmPl+VyHN5oBqQA73uamt4VkSpuB5UXVU3CuQvaByQDJ1X1W3ej8kodVU32PD4I1HEzmCIYDXztdhAFEZEbgSRVXed2LF5oDfQVkZUi8pOIXOZ2QIV4HHhRRPbj/N355NNdSCR6ERkKHFbVeLdj8VI5oCvwtqp2Ac4SOE0Lv+Bp274R582pPlBFRO50N6qiUWcMccDceeZHRP4IZADT3I4lPyJSGfgDTrNCMCgH1AR6Ak8Bn4iIuBtSgR4AnlDVRsATeD71l1RIJHqgD3CDiOwBpgNXish/3A2pQIlAoqqu9DyfiZP4A9HVwG5VTVHVdGA20NvlmLxxSETqAXi+B8xH9ryIyChgKPAbDezJLS1w3vTXef7eGgIJIlLX1ajylwjMVscqnE/8AdF5nI97cP7GAD4FrDM2m6o+o6oNVbUpTkfhD6oasHedqnoQ2C8ibTybrgI2uxhSQfYBPUWksudO6CoCtOM4ly9w/mjwfP/cxVgKJCKDcJodb1DVc27HUxBV3aCqtVW1qefvLRHo6vk/HYg+AwYAiEhroDyBXcnyANDf8/hKYLsvTlrOFycxxfIIME1EygO7gHtdjidPqrpSRGYCCTjNCmsIsCnlIvIxcAVQS0QSgeeAF3A+pt+HU/J6uHsR/lc+sT4DVAAWeFoVVqjqeNeCzCGveFXVJ80JvpbP73YKMMUzhDENuCdQPjHlE+/9wKuegQ8XgLE+uVaA/MzGGGP8JCSabowxxuTPEr0xxoQ4S/TGGBPiLNEbY0yIs0RvjDEhzhK9McaEOEv0xhgT4v4/LJNqTR40464AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 01_maximum_minimum_temperatures\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "x = np.arange(4, 19)\n",
    "y_max = np.array([32, 33, 34, 34, 33, 31, 30, 29, 30, 29, 26, 23, 21, 25, 31])\n",
    "y_min = np.array([19, 19, 20, 22, 22, 21, 22, 16, 18, 18, 17, 14, 15, 16, 16])\n",
    "# 绘制折线图\n",
    "plt.plot(x, y_max)\n",
    "plt.plot(x, y_min)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.2　绘制柱形图或堆积柱形图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2.1　使用bar()绘制柱形图或堆积柱形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "x = np.arange(5)\n",
    "y1 = np.array([10, 8, 7, 11, 13])\n",
    "# 柱形的宽度\n",
    "bar_width = 0.3\n",
    "# 绘制柱形图\n",
    "plt.bar(x, y1, tick_label=['a', 'b', 'c', 'd', 'e'], width=bar_width)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(5)\n",
    "y1 = np.array([10, 8, 7, 11, 13])\n",
    "y2 = np.array([9, 6, 5, 10, 12])\n",
    "# 柱形的宽度\n",
    "bar_width = 0.3\n",
    "# 根据多组数据绘制柱形图\n",
    "plt.bar(x, y1, tick_label=['a', 'b', 'c', 'd', 'e'], width=bar_width)\n",
    "plt.bar(x + bar_width, y2, width=bar_width)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制堆积柱形图\n",
    "plt.bar(x, y1, tick_label=['a', 'b', 'c', 'd', 'e'], width=bar_width)\n",
    "plt.bar(x, y2, bottom=y1, width=bar_width)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 偏差数据\n",
    "error = [2, 1, 2.5, 2, 1.5]\n",
    "# 绘制带有误差棒的柱形图\n",
    "plt.bar(x, y1, tick_label=['a', 'b', 'c', 'd', 'e'], width=bar_width)\n",
    "plt.bar(x, y1, bottom=y1, width=bar_width, yerr=error)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2.2　实例2：2013—2019财年阿里巴巴淘宝和天猫平台的GMV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 02_taobao_and_tianmao_GMV\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "x = np.arange(1, 8)\n",
    "y = np.array([10770, 16780, 24440, 30920, 37670, 48200, 57270])\n",
    "# 绘制柱形图\n",
    "plt.bar(x, y, tick_label=[\"FY2013\", \"FY2014\", \"FY2015\", \n",
    "                          \"FY2016\", \"FY2017\", \"FY2018\", \"FY2019\"], width=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.3　绘制条形图或堆积条形图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3.1　使用barh()绘制条形图或堆积条形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "y = np.arange(5)\n",
    "x1 = np.array([10, 8, 7, 11, 13])\n",
    "# 条形的高度\n",
    "bar_height = 0.3\n",
    "# 绘制条形图\n",
    "plt.barh(y, x1, tick_label=['a', 'b', 'c', 'd', 'e'], height=bar_height)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.arange(5)\n",
    "x1 = np.array([10, 8, 7, 11, 13])\n",
    "x2 = np.array([9, 6, 5, 10, 12])\n",
    "# 条形的高度\n",
    "bar_height = 0.3\n",
    "# 根据多组数据绘制条形图\n",
    "plt.barh(y, x1, tick_label=['a', 'b', 'c', 'd', 'e'], height=bar_height)\n",
    "plt.barh(y+bar_height, x2, height=bar_height)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制堆积条形图\n",
    "plt.barh(y, x1, tick_label=['a', 'b', 'c', 'd', 'e'], height=bar_height)\n",
    "plt.barh(y, x2, left=x1, height=bar_height)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 偏差数据\n",
    "error = [2, 1, 2.5, 2, 1.5]\n",
    "# 绘制带有误差棒的条形图\n",
    "plt.barh(y, x1, tick_label=['a', 'b', 'c', 'd', 'e'], height=bar_height)\n",
    "plt.barh(y, x2, left=x1, height=bar_height, xerr=error)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3.2　实例3：各商品种类的网购替代率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 03_substitution_rate_online\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# 显示中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "x = np.array([0.959, 0.951, 0.935, 0.924, 0.893, \n",
    "              0.892, 0.865, 0.863, 0.860, 0.856, \n",
    "              0.854, 0.835, 0.826, 0.816, 0.798, \n",
    "              0.765, 0.763, 0.67])\n",
    "y = np.arange(1, 19)\n",
    "labels = [\"家政、家教、保姆等生活服务\", \"飞机票、火车票\", \"家具\", \"手机、手机配件\", \n",
    "          \"计算机及其配套产品\", \"汽车用品\", \"通信充值、游戏充值\", \"个人护理用品\", \n",
    "          \"书报杂志及音像制品\", \"餐饮、旅游、住宿\", \"家用电器\", \n",
    "          \"食品、饮料、烟酒、保健品\", \"家庭日杂用品\", \"保险、演出票务\", \n",
    "          \"服装、鞋帽、家用纺织品\", \"数码产品\", \"其他商品和服务\", \"工艺品、收藏品\"]\n",
    "# 绘制条形图\n",
    "plt.barh(y, x, tick_label=labels, align=\"center\", height=0.6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.4　绘制堆积面积图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4.1　使用stackplot()绘制堆积面积图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "x = np.arange(6)\n",
    "y1 = np.array([1,4,3,5,6,7])\n",
    "y2 = np.array([1,3,4,2,7,6])\n",
    "y3 = np.array([3,4,3,6,5,5])\n",
    "# 绘制堆积面积图\n",
    "plt.stackplot(x, y1, y2, y3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4.2　实例4：物流公司物流费用统计## "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 04_logistics_cost_statistics\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "x = np.arange(1, 13)\n",
    "y_a = np.array([198, 215, 245, 222, 200, 236, 201, 253, 236, 200, 266, 290])\n",
    "y_b = np.array([203, 236, 200, 236, 269, 216, 298, 333, 301, 349, 360, 368])\n",
    "y_c = np.array([185, 205, 226, 199, 238, 200, 250, 209, 246, 219, 253, 288])\n",
    "# 绘制堆积面积图\n",
    "plt.stackplot(x, y_a, y_b, y_c)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.5　绘制直方图# "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.5.1　使用hist()绘制直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 准备 50个随机测试数据\n",
    "scores = np.random.randint(0, 100, 50)\n",
    "# 绘制直方图\n",
    "plt.hist(scores, bins=8, histtype='stepfilled')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.5.2　实例5：人脸识别的灰度直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 05_face_recognition\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# 10000 个随机数\n",
    "random_state = np.random.RandomState(19680801)\n",
    "random_x = random_state.randn(10000)\n",
    "# 绘制包含 25个矩形条的直方图\n",
    "plt.hist(random_x, bins=25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.6　绘制饼图或圆环图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.6.1　使用pie()绘制饼图或圆环图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "data = np.array([20, 50, 10, 15, 30, 55])\n",
    "pie_labels = np.array(['A', 'B', 'C', 'D', 'E', 'F'])\n",
    "# 绘制饼图:半径为 0.5,数值保留1位小数\n",
    "plt.pie(data, radius=1.5, labels=pie_labels, autopct='%3.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "data = np.array([20, 50, 10, 15, 30, 55])\n",
    "pie_labels = np.array(['A', 'B', 'C', 'D', 'E', 'F'])\n",
    "# 绘制圆环图:外圆半径为1.5,楔形宽度为0.7\n",
    "plt.pie(data, radius=1.5, wedgeprops={'width': 0.7}, labels=pie_labels,\n",
    "        autopct='%3.1f%%', pctdistance=0.75)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.6.2　实例6：支付宝月账单报告"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 06_monthly_bills_of_alipay\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "mpl.rcParams['axes.unicode_minus'] = False\n",
    "# 饼图外侧的说明文字\n",
    "kinds = ['购物', '人情往来', '餐饮美食', '通信物流', '生活日用', \n",
    "         '交通出行', '休闲娱乐', '其他']\n",
    "# 饼图的数据\n",
    "money_scale = [800 / 3000, 100 / 3000, 1000 / 3000, 200 / 3000,\n",
    "               300 / 3000, 200 / 3000, 200 / 3000, 200 / 3000]\n",
    "dev_position = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]\n",
    "# 绘制饼图\n",
    "plt.pie(money_scale, labels=kinds, autopct='%3.1f%%', shadow=True,\n",
    "        explode=dev_position, startangle=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.7　绘制散点图或气泡图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.7.1　使用scatter()绘制散点图或气泡图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10ba4e20>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num = 50\n",
    "x = np.random.rand(num)\n",
    "y = np.random.rand(num)\n",
    "plt.scatter(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x109cd580>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vFZex/3j0x89VME8wf/t4L+sO1PDbd2Lzad6X3f/hHjwhBPITvH6Dhz7dF8UWmWfl3io6G0HdXlGPuxvb2HuzSfkZ3LpgBBYtOkUj/IbkiZUHonLuk6mZkASzaF4hFXUubpo77CuvSSnZUl7Po8tLWFda80Wyn2S7hRkFWdw0r7BHT9bF0tEGN6tLqrs1VhqQkve2VfIrl490Z3w3iHTF6GIuTCA6DfZ9zUsbyvEb0fmB+A3JqxsP87//MR6rHr3+swrmCWZWYTb/vmveKf9PSslrmw7zwCf7qKx34/EHOPnvrsUb4L1tR/hk1zH6pzv4/lkjuHjyoKiVp+oNPt11LKy8HFZd47N9VZwfhRqOZppekMXKfVUdvj6sX5Jpk3I9XYvXH/W0tpoGe442Mn5gevSuEbUzK6bwBQx++MJmfv7aNg5UNePynRrITzgxWXegqpmfvbqNH720BX8g9CGEvqbO5cMXxs8nYBjUtfii0CJz3XX2yA4ryTutOj/62ugYtyhx7TzSgMMa3Q82wwhOSkeTCuYJTErJnc9u4t/bK7uVptPlC/De55Xc+fymhMvsligcFg0tjDFSTYgOg2QiKSrI4rcXT8Bu0b5or92iYbdofP+sEVGpDt9Tlde6kFHO2u3yBSivcUX1GmqYJYHd/9Eelu05HtJqi7ZcvgBLdx3nbx/v5Qdnj4pC63q2MQPSsGgCbze/z5DBuo89wbemDeassbm8sbmCA1XNDMxwcPGUQQlb9ixeAoaMSQUGT5SflFUwT1AtXj+PLj/QrR55WyfyLi+aN1yNj7Yxc1gWaU5rt3f/Dc5yMm5gzwjmABlJNq6bXRDvZiQ0p1WP+vySJiA1yjtvE/95sY96fdNhU/JGCAFvbjnc9YF9jBCCW+YV4uzGkInTpnPbguFRbJUSDyPzUrtc/ROpJJuFkXmpUb2GCuYJSErJkmUlpuSMaPEG+PvS/WrsvB1XzxrKlCGZOCxdvw0cVo25I/px0aRBMWiZEkuF/ZKjtizxBF/AYMLg6K1kARXME1J5rYujjeEnzm/rSL2bw3XRnXzpiSy6xhPXT2f2iH4k2fR2n4Q0EXwM/9q4/jx01dSwJk2VxKZpgtmF2UTzN9svxc7A9OjOVagx8wRU0+zFqmthTXy2x6pr1Db7GJw4Fa4ShsOq8/h1RRQfrOWR5SUs3X0Mixbs4wSk5Gvj8rhpbiGT1GasXm3R/ELWldaYnkERgp2BW+YXRn1cXgXzBGT2NmshwO1XW7c7IoRgekEW0wuyaPH6qW7yIgRkJ9vVxHEfcXphNgPSHZQcbzZ9YYtVF3xz6mCTz/pVapglAaU6rKZutZaSuNcn7CmSbBbys5IYnKl2SPYlQggevHIq9hDmT7rDadX547cnkRKDHPIqmCegQRlOvCauSfUGDPpHebxOUXq6sQPSuOPMEThN2g3qtOqcNTaXc8f3N+V8XVHBPAGlJ1mZPyrHlKWJmoCFo3MTPjGUoiSC2xeO4LLpgyMO6E6rRlFBJvdfPtmchoVABfMEdfO8QlN6CA6rzqJ5hSa0SFF6PyEE/3vheH54TjC3TTiLlxxWjW9Ny+eJ66dHNUtiW31yArS6ycPnh+uprHfjNyTJdp3ReWmMzEuJ6Q+/M9OGZtIvxc6hCLO55aTYmTokw5xGKUofIIRg0bzhnDkmj7te2MT+Y814/QaBLiaykm06GUk27r98MjOGxb7EYJ8J5lVNHp5bd4inVh+krsWLw6LjNyQSiS4EiGDxgZnDsrl5fiFnDO8X1zXFQgj+csVkrnp0bdhb+h1Wjb9cMVmlwlWUMIzITeHtO+ey7XA9j604wPK9x2l0+07JsOj1G1h1jekFWdw0dxinD8+O2/ut1xd09gUMHvh4L0uWlwB0WErrZMk2ndw0Bw9dOTXueTg+2F7J95/f1O015w6rxoPfmcrZ4/Ki1DJF6Xtqm73sP96E22dg1QVDs5PJS7PHLIB3VtC5VwfzspoWrntiHUfq3d3u3QrAbtW4Y+EIbl84Iq6929X7q7npqWIMKbvc1JBk09GE4PHriphZmB2jFiqKEgudBfNeO8xysLqZix/6jHqXr91iDl2RBAvfPvTpfqqbvPzywnFxC+inD89mzc/O4vVN5SxeWkJNixev3/gin4RFE9gsGlnJNm6dP5yLpgyKybrWvqS8toUnV5Xy4c6juL0G2Sk2rpo5hIunDCLJpn7WSvz1yp55o9vHWX9eRlWTJ6xA3pbTqvOTc0dzw5yv1uWMNSkl60tr2XiolppmDyDISrYxbWgmRUMz1fh4CH7/7k6eX1/Gj84ZxbVdpIc1DMnv3tvJv1YfxJASX+DLP6gkm44A/n71NOaNyoluoxWFPjjM8qMXN/P21iMhjY+HymHVeO8H8xjWL9m0cyqxJ6Vk+M/exZCQk2pn/c/P7vT4X7+1nefWHcLVyZyFw6rxz+/OYJYa1lKirLNgnhjr8Ey04WAN735eaWogh+Cs9V3PbzLlXG5fIFjdRIk5IQRXzBiC3aJxQxe98v3Hm3h2beeBHILDcT96cYtKM6zEVa8L5g9+ss/0RFUQLBe2+2gjO480RHSeDQdrmfi/H7Dgj59GpZ1K1373zQnsvuc8bl04otPj/vHZgZDzXNe2eFl3oMaM5ilKWEIK5kKIx4UQq4QQv+jiuIeFEBea07TuO9bo5rP91VEr5+cLSJ5YeSCic2yvqAcklQ1u6l2JX+W9L3t/+9GQg7nLG+DDHUej3CKlJ6tt9rLnaCOlVc34olAPtMtpeCHEJYAupZzdGqxHSin3tnPcXKC/lPIt01sZorUlNVjDKNIbqoAhWbr7eETnuKwon5pmL8NzUshLU8mvEll3npwkUO/uHR/OUkp2VTaytbyO4tJaymtd+A2DZJvOmP6pjMxLY+awLAZlOtWEexeklCzfW8XipfvZcLAWq0UgJWhCcPWsIXz3jGGmxYFQ1lQtAF5s/foTYA5wSjAXQliBR4F3hRAXSSnfaHsSIcQiYBHAkCFDImhyxzYeqo1KcvmT1bZ4aXD7wk4p67Dq3HX2KJNbpURDmsNKo9sf0rG6gNxUe5RbFF0tXj+vbixn8bISapq9SMlX9mcs3VP1xddOq8a1pw/ljjNHkqpSLH+FlJL/fn0br246/EVcOjk8PbGylGfWHuLp7800pfhJKMMsycCJisANQHtbCq8FdgD3AjOEEHe2PUBK+YiUskhKWZSTE51lXNsO10dtiOUEp02n5HhzlK+iJILLigaHnN/aatG4aHLPrQ+6al8Vc//wKb99ZxfltS5avIEuN9q5fAZLlh9g8q8/5J2tFTFqac9x/4d7eGXj4Q47mN6AQaPbz1WPraUswhxMEFowbwKcrV+ndPA9U4BHpJSVwNPAwohbFgazyqx1fR01cdkXXDlzaEjHaSKYx2NUlKuvR4NhSH75xjZueHI91c3esPIABQzJ7c9u4rEVJVFoYc/U4PaxZHlJSD9Pl8/PQ5/ui/iaoQTzDQSHVgAmAaXtHLMPOJFntQg4GHHLwmCLUcbDRMmsqERXTqqd31x8Gg5rx79vISDFbuGhK6fGsGXmMAzJD1/YzEvF5aZ0hO55Zycf71STwACvFJejhTifEDDg9c2HafaENqTXkVCi0uvANUKI+4DLgO1CiHvaHPM4sFAIsRy4DfhTRK0K08i8lKhfw+MzGJKVFPXrKInhsqJ8/njpRNIcFpJPKiOnieDO4JG5KbxxxxyGZve8zWT3fbiHD3YcDTsrZ3tueXoDRxvcpp2vp/p417Fu/Vytmsb2isiWPXc5ASqlbBBCLADOAe5tHUrZ0uaYRuDbEbXEBNOGZvLmloqoToI6bTo5PXyiS+meCycN4uunDeCD7Uf5cEclTR4/A9KdfLtoMBMHZ8S7eWEJpnUtwW3y5jpfQPL95zfx/E2z+vRKF1d3Y5CIfPg2pAxBUspavlzRkrBmDMuK6s5KAUwvyIza+ZXEZdU1vjFxAN+YOCDeTYmYYUhuf3aj6YH8hM0H63h76xEunDQwKufvCfLSu9fhCxiS7BRbRNfsVenehmYnM6Z/KlvK66NyfqdN58a5qgRbIjpQ1cziZft5Z+sRXL4Aeal2bpgzjCtnDlFZDdtYtb+aqkZP1M7vCRj85aM9fTqYX1aUz7Ldx2kOsYee7rQybkBktRN63Uze7QtHkGQzp7p2W9kpNmbGoRyU0rk1JdWc/9cVvFxcRpPHT8CQVNS7+dP7u7nwgZU09JLNPGZZsnx/yEEmXBV1braU1UX1Gols3sgckkNMQ+20aiyaWxjxsFSvC+bnjMtj4uB0LCaXfHNYNO6/TJVgSzQtXj83PlmMyxcg0GaEze03KKtx8dNXP49P4xKQ12+wen911K/j8Qf497bKqF8nUWma4OGrpnZZlN1u0RjTP42rZoW2DLYzve75UwjBXy6fwln3LcXvMaf34bBqXFaUT1GB6pUnmjc3V2B0kq3QGzD4aMdRqps8ZKf0nonrreV1PLbiANsr6mnxBnBYdYZkJXH97ALmjcpB76Azs+doIw6rRpNJ742OGBLW9vHEY0UFWfzzu9NZ9K8N+APGKU9DuhZcwXL68GweumoqthA3p3Wm1wVzgP7pDp787gyueXxdxMuuHFaNoqFZ/PKCcSa1TjHTp7uPdbl6yaZrbC6r46yxPb8e6jtbj3Dfh7upqHPj8QdOKb5yoKqZ4tIaHFadm+YVcuOcYVja7InYUdFgSsGWUOw52hibC0WZL2BQ2+zFZ0gynNaQh08AZhZms/7nZ/P+9kqeWl1KZb0bq0Vj5rBsbjijgJEmbjTrlcEcgp+KT984g+v/sR63L3BKhZhQOa06C8fk8JfLp3zlTaEkhlBTiPf0VONSSu55Z2drfvWOP7yavQGavQH++tEeVuw5zmPXTcd50hxSvcuHN0qrWNrq9vK8BLP3aCOPrzzA65uD2UwEAl/AYMqQDG6ZP5wFo3M7fAI6mc2iceGkgVGfEO7VEWra0CyW/WQhC0bl4LTqhDrc7bTqpDos/PmySTx81TRTHoGU6Jg/KqfLCW9vwDAlkVE83fv+7i4D+clcPoPig7V878n1+E9Ktyqjnr3oS7G8lpn8AYO7X9nKhQ+u5KUNZbh9Bm6fgcsXwG8EyzZ+/7lNnPXnpRypd8W7uV/o9VEqK9nGo9dN5+kbZ/K1cXnYdI1Uu+WUrf8CSLbrOK06ual2fvS1Uay8+0zOn9Dz1xT3dhdPGURnn9FWXbBwdE6P3ui1an8V//ystNtDhh6/wcZDtSxZ/mXOlDSHNWbpKJw9cEmolJI7n9/Em5sP4/YZdJR2vNkboKzWxYUPrORYgux47Xk/7TBNG5rJkmuKaHT72F7RwLbD9RyqacHrN4JrPAemcdqgdAr7JasVKz1Ist3C4mumseipYjx+45TxYLtFY0C6gz98a1L8GmiChz7dF/bcj9tn8OiKEm6ZPxxdE4wbmBbS0IAZRuZGP72G2Z5de4ilu453WSoQght96lp8LPrXBl6//YwYtK5zfSaYn5DqsDKrMFsV3+0FGt0+Xt14mEeWl+D1G6f00HVNcNaYXH598WmkJ/XcXNsVdS7Wl9ZGdA6f3+CTXcc4Z1weo/unxiTrpyZI+D0ZUkoOVre0pmdwkJVs46Gl3fvg9BuSXZUN7K5sZHT/+GbN7HPBXOn5PP4Av3lrJy9vLEMTot3VLAFDsmzPcebd+yk3zhnGXWePQotRj9RML28oJ9Kh52ZvgH98doBzxuVht+gUFWSypiS6ywYdVp2vje8f1WuES0rJC+vLeOCTfdQ0e9E1gTdgMDovlZqm7tcp8wUMHltZwh8vje8ToArmSo/S4PZxzWNr2X20scu0rSfW9T664gDbjzTw9x44mb3/eBNeE+pFnlz84Ob5w/m8vD6qu0BzUu1MHZIRtfOHS0rJf73yOW9uqfhKD/zzw+GlAQkYsHxPZOUkzdCz/rKVPs3rN7juiXXsrOw6kJ/M5Qvw2d4qfvDCJmQPW6PYFGLZuq6cHLjmj8whIymypE6dcdp0fnDWyISce/pgx1He2vrVQB6plihvwgqF6pknGCklm8rqWLm3iuONHiSSnBQHs0dkUzQ0MyHfILFy/0d72HmkIax10m6/wdJdx3l+fRnfmRGdGrTRkO40Z7w/+aSVJZomeODKKVz56BrTq3PpmmD8gDS+OSU6JfSkDC4NXLn3OGsP1FBS1YzPb2DRBYMynMwYlsWswmzmj8ppd2/Iw5/ui0qKbHsnBUxiRQXzBOHyBnhzy2H+vnQ/xxo9uH1f7u7TBCxZrpOVbOOW+YV8c8rgbu1C6w08/gBPrT4YUfBx+QI89Ok+rpie32M+FMcPTOPdbUcium8BjB5w6uTc1CGZXD1rKM+sCX3teigcVo2/fmeK6T/fgCF5bu0h/r5sP7UtXjzt5OKpavLy+eF6nl13CKumcf0ZBSyaV/hF1syAIdka5lBKV8YPTI/Kebsj/h8nCgerm1n456X86q0dlFa30OI9dZu2IaHFG6C81sXv3t3F/D9+yr5jTfFrcBz8e1ulKds4a5q9FB+MbHVILH1r2uCIb9tp07lxzldTN//0vLHMHdkPp0m9yiSbzlM3zGBQhrPrg7th37Emzv/rCn733k4O1wWLTXe0oduQ0OwJUOfysXjpfhb8cSlrS4KJxfyG0emehHAl23Runhf/1NgqmMdZaVXzFxsPQnn8a/EGqG7ycvFDn/Wa3BehWLzUnLStLm+AJcv2m9Ci2MhIsvG1cXlEshAnK8nWblEVvTWz3/kTBnSZ3a8zNl2Q5rDw9I0zmTbU3OWIn+46xoUPrGTvscZuD4+4/QbHGj1c9491PL6yBLtFJzvZ/M1jaU4rpw+P/1JnFczjqMHt4/Ilq2ny+LuV/EgCzR4/Vzyyhprm7i+l6on2HTfnSUQS/qqFeLl1wYiwV+E4rTp3nd3xZKRF1/jzZZN58MoppDutOLpxHdF6/gWjc1n2k4VMHWJuFa6lu49x6zMbcPkCESUHc/sM/vj+bp5YeYAb5hR0eY8CCPXH4LTqLL56WkIM26lgHkfPrztEvdsX1h/qiYD+5KpSs5uVcHwBA7+Jqf6aE2DlQXeMG5jGPRedhqObwyFOq85FkwfyrWmDuzz2rLF5LP9/C7nrnFHkpNhJtuvY24loQkCK3YLNorFgdA7//O50Hrm2iMxkc1fHVNa7ue2ZjaZN0Lp9Bve+v4uJgzPISbNj7eBRx2nVueb0oYzMTT0lQVlbNl2QbNd54vrpCZP3p2/NoiUQw5A8uuJARH+sHr/Bk6tKufPMEb06q6Nucq/H7MIlsXBpUT4S+O83tuFtk7agPU6rzreLBvO/F44PudeY7rRyy/zhLJpbyObyOraW1bHuQA2H6934AwZJNp3J+RlMGZJJUUEmuamOyG+sHVJK7np+k+nZHd0+g5+8tIWXb53NXc9vYmt5PX5D4jckTquORHLT3GH88JxReAMGb2yu4O9L91NZ725NgSChddT9qplDuP6MAgakmzs/EAkVzONk5b4qWjyRryH2GQYf7zrGuQm6284MmiZwWHTTVl2kOnrmn/23i/IZOyCNh5fu46Odx9DglKLMFk2ga4IJg9K5beFwzhwTXv52TRNMHZLJ1CGZXH/GsC6Pr2vx8tt3d7LvWBNFQ7P40ddG4YhgDH7F3iq2Hq439WnshNoWL+9+foQXb5nNvmNNfLCjkia3n/ysJC6YOIBUR3ApqN2ic1lRPpcV5bOjooFDNS34AgbZyTamFWRit0SnNGUkeuZfdZz4A8EekRm7CN/9/IgpE3rNngBvbano1cEcYMHoHN7fXhlxYQWbLnp0NszTBqXz8FXTqGn28mJxGdsO11PX4iPFbmFYv2Qun55PQb/kmLXHFzC4dPFqDlY34wtIdlY0UHK8icevnx72OZcs2x+VteAQTA38yPISbjhjGCNyUxiRO6LL7xk3MI1xAyMrthwLKpiHoKbZy89f+5wPdxzFkJIpQzL5w7cmMCI3/MQ6x0ysjh7NSuuJ4qZ5hSzbczziN7kQgmtnF5jTqDgK7jkYHu9msPdoExV1ri+Kv7j9Bkv3HMflDXQ65tyRqiYP66O8dLTZ46f4YC0zEjwRWHf13oFWkxiG5PIlq/lo51H8hsSQsPFgLZc8vIraCFaS+A3zxgOj8TiaaKbkZ5BrQk7yoqGZpq+DNtu6AzWc9j/vs+Fg4tfQtFlEu+vgw02zu6WsLur5c7wBo0f8bLtLBfMurCmpPqXnAcFpEG/A4MXisrDPm23i7H9mD07xGiohBD89f2y3V3SczGHV+PG5o01sVXRkJFkZnpNs2lb+aBqek8KMYVlfbDxyWnWun13QaUA2DElNs5ejDW5qmr0YJ3VGtpTV4TJhLqkzvoBkbZSzRsaDGmbpQklVM4F2uh5un8GuyvA37Zw+vB/vbz8a8bBBkk3njBH9IjpHT3Hu+P7ctmAEf1+6v9uToQ6rxu8unsAUk9dCR8OovFTeuGNOvJsREiEEj19XxPPry9h/rIkpQzO5cOJX5yT2HWvi+fWHWL2/mn3HmpCALiAgg+tDRuSmcPrwbPZUNna4u9NMh07KItlbqGDehdH9U9HaWdrltOpMGhx+PoYLJw7kf9/cHknTgGC+iVDWEfcW3z9rJA6rxv0f7g0poGsiOGF977cm8h+To5P8qa+z6BpXzxra7mvrS2v4zds72FPZ+MUywPZsrwgWeDBilNWyNw5NqmDehaKhmYzITWFXZeMX6151IUiy6VwSQRB12nQunTaY59YewhfmH5auCS6cNPCL5VR9xaJ5w5kyJJMHP9n7RZEFT5s1yQ6rhpRw9tg8bl84okesRuhNWrx+7nl7J69uKg95L0UsA2wkw3WJSgXzLgghePamWfz+3Z28vukwvoBk/ugc/ufCcaRFGERvnFPIyxvK8YU51GLTNW6ZH/8EP/EwvSCLJ2+YSWW9m6dWl/LZvirq3T40BOlJVr42Lo8rpg8xfWei0rVjjW6+vXg1R+vdp6yDTySJkOXQbCIeyfqLiopkcXFxzK+biD7dFcw/0d2doA6rxn2XTeL8CQOj1DJF6b6aZi8XPrCCow2ehB3KcFo1/vuC8Vw5s+fktT9BCLFBSlnU3mu971mjh1k4JpcHvzMVp1UPKTOeIBjI/3ipCuRKYpFScvO/ijmWwIEcgmlyzxgR/yyHZlPBPAGcPS6P128/g6+P74/NorWb1c1u0bDpGmeNzeXlW2Zz4SQVyJXE8ty6MrZVNIQ9BxQrpw1KZ2h27HbJxooaM08Qo/un8vDVwW3aL6w/xL+3VVLn8gHBBEhnjcnlyplDyTFh44yimK3e5eM3b+8wvbam2ZJsOnec2fUW/p4opGAuhHgcGAu8K6W8p5Pj8oB/SymnmNS+Picr2catC0Zw64Le+Qen9E6vbCiPdxO6ZNUFs4dns3B0brybEhVdDrMIIS4BdCnlbGCgEGJkJ4f/CUjsvdKKophKSskjK0p6QK/cwr2XTop3M6ImlDHzBcCLrV9/ArS7NU0IcSbQDFR28PoiIUSxEKL4+PHjYTRVUZREVF7roq4lsSteJdt1nrtpFlm9eKlqKMMsycDh1q8bgK88/wshbMAvgYuB19s7iZTyEeARCC5N7H5T4297RT1Ldx+nuimYpbBfqp2Fo3MZO0BtSFH6rm2H67FoGpB4a8odFo2U1vqkY/r37vdpKMG8iS+HTlJovzf/X8BDUsq6RKiFZyav3+CdzytYvKykNWezQaD1b1bXBH/7eC8F2cncumA4508YgLUXV/xRlPZsr6inxRvd5FjdpWtg1TUunjyI/75gHMn23r/WI5Q73EBwaGUNMAnY3c4xZwNnCiFuByYLIR6TUt5oXjPjo67Fy1WPreVAVXO7CbEChiRgSHZVNvLTVz/n8ZUH+NcNM0nvA1kMFeWEmubw6tiGy6oLbK2dppMLvDitOrom8BsGF08exPfmDGNkXvg1B3qaUIL568AKIcRA4DzgCiHEPVLKX5w4QEo578TXQoilvSGQN7h9XPTQZ19Jf9uRFm+AXUcauPjhz3jrzjmk9IGegKJAsMhzLH27aDDXzx7G1vJ69h5tpNnrx2HRGZaTzIRB6Yzun5qQZd2ircuII6VsEEIsAM4B7pVSVgJbOjl+gVmNi6db/rWBI/XukAL5Cd6ApKLOxW1Pb+Sp782IYusUJXFkJdvQBDHpnWsCclIcjMpLZVQf6nWHIqQBXillrZTyxdZA3uvtqmxg46HasKqDe/wG60qr2Xcs/FznitKTnDYonSRbbJ5Ek20WxqsMmO1Ss3XteGzFgW71yNvyBQweX1lqXoMUJYFNGJSOLxCblSzegMGECOoI9GYqmLfR5PHz9pYKAhE8MwYMeG1TOa4oVRgPx5Jl+7n7la00R7kkV2de21jOt/6+it++szNmb34l+gakO0ypzxqK3FQ7/dMcMblWT6OCeRulVc1YTFheaNE0ymoTozTV8UYPf/j3Ll7dWM772+MzUrbtcD0/e20bGw7W8q81pTy2oiQu7VDMJ4TglvnDSbJFd9IxyaZzy/zh9Lblz2ZRwbyNRrfflNl5IaChNVFWvGUn2/jauP4Mz0lh9vD41As9XOf6IsWv22dQWp0YH3SKOS6eMijqQVYTgounqNJ/HVHr59owq5yUlOCwJsbyKE0TLL5mWlzbMGdEP3LTHBxrcANw7ent14xUeqZku4XfXjyen766LSo5WpxWnd9efFqf2PwTLvWTaaN/uiOsVSxtuXwBctNUutoTku0W3vvBXPYebWJwplOVc+uFLpo8iFc2HmZtSQ1eE+dEbLpGUUEm/zFZ5fDvjBpmaWNAupPR/SNfv2oYEk83S8H1dg6rzoTB6SqQ91JCCB66aiqDs5xYdXOGXKy6YHCWk4eumqrGyruggnk7bp0/nGR7ZEMkmgZPri41p0GK0kOkOay8eutsCnNScEY4zOi0ahTmpPDarWdEXDy9L1DBvB1nj8v7IvdDuAIGvPd5n9hjpSinyEiy8dYdc7h+dkG7JRBD4bBoXD97GG/dMUflOgqRCubtsOoaj15bFPFkqDvBk/UrSrTYLBp3nzeGl2+dzdyR/YI1bLsI7DaLht2iMXdkP16+dTZ3nzemy+9RvqQmQNvR6PYhhOD2BSN44NN9YU+IqnqdSl932qB0/vW9mRypd/HqxsOs3FvFjiMNNLh96EIQkJI0h5VxA9KYM7Ifl0wdxIB0VawsHCqYt9pd2cgTnx3g013HqGn2Bsf7BFg1gT+MJEJJNp3vnlEQlbYqSk8zIN3J7QtHcPvCYG0bX8DAFzCw6pqqAWCSPh/Md1c28uOXtrD3WCM+v8GJlCyNEW57t1s0LpykllIpSntUEDdfnw3mAUPywCd7WbxsPx6fgVnZO626wGnVefamWTHLJKcoitIno40vYHDL0xtYta8atwlrwS2awKprCAGXT89n0bxCNe6nKEpM9blgbhiS257ZyGf7qkwJ5BCsN/j9s0bwvTmFavZdUZS46HOR56nVpazca14gB/D4JX/9eC+l1c2mnVNRFKU7+lQwL6tp4Q//3h2VREAev8FtT2/Ar/J0K0pIjjd6eGVDOU1xzLHfm/SpYP6bd3aYkkSrPVJCRb2bNzZXROX8itLb3P7sRu5+ZSv3vL0j3k3pFfpMMK9q8rBs93ECMnpVZ1u8ARYv2x+18ytKbzJpcAZ+QzJRlYEzRZ+ZAH15Q3lMrlNe62LnkQbGDlBFZxWlMz//xlju/vpoUyp7KX2oZ7509zE8URpiOZlEUlxaE/XrKEpv0BMDeb3LR1lN4lXK6nk/yTDtqGiIyXXcPoO1B1QwV5Te6qIHVzL33k85UJVYq9f6xDBLk8dPizd2GQx3VzbG7FqKosTWuIFpeAMG6c7ESs3bJ4K52xdA1wT+7mbLClM0lj4qipIYHr4qvvV0O9InhlksmiCKi1javZ6iKEos9YlgnhrjklMqL4uiKLHWJ4K5rgmG9UuOybUsmmBWYXZMrqUoinJCnwjmADOGZRGL0Q+HVWfykIzoX0hRFOUkfSaYXz49H7slsmrhoRACZg9XPXNFUWKrzwTz0walMygzumPZNovGNbOGqgoqiqLEXJ+KOj89b0ywtmeUWDTB9arup6IocdCngvlZY/OYPyoHm27+4LnTqvObi04jN9Vh+rkVRVG6ElIwF0I8LoRYJYT4RQevpwsh3hNCfCiEeE0IYTO3meb5/SUTSHNaTZ0MdVg0ZhVmccnUQeadVFEUpRu6DOZCiEsAXUo5GxgohBjZzmFXAfdJKc8BKoGvm9tM82Qm23j5ltmkmxTQHRaN8YPS+fvV0xBCbRZSFCU+QumZLwBebP36E2BO2wOklA9LKT9s/c8c4FjbY4QQi4QQxUKI4uPHj4fZXHMU9EvmzTvmUJCdHNEYutOqcdbYPJ69aSaOKI7FK4oSGl/A4GiDm7KaFirr3VErRpOIQsnNkgwcbv26ARjR0YFCiNOBTCnlmravSSkfAR4BKCoq6vbmeiklbp+BzzCw6VrEwTM/K4n3fziPBz/Zx+Jl+5ESvCGWfEuy6Vh1wZ+/PZmzx+VF1A5FUcJnGJLP9lfx5uYKNhyspay2BV0TaEJgSIk/IBmc6WTqkEwumDSA+aNy0Xtpuo1QgnkTcGJNXwod9OaFEFnAA8C3zGka7DvWyMsbyllTUsOeo414/AaagIAhSXVYGTsglbkjc/jW1MH0T+/+xKNV1/jhOaO4fHo+T685yL/WHMSQEsM4NVmWJiDJZsFvGOSlOrh5fiEXTxlEkq1P5ClTlJBUN3n4/HA9B6qa8Qckmck2xg9MY2Ruiul5y71+g3+tOciSZftp9vhpPikrqi9wal+xtLqF0uoW3t9eicOq8725w7jhjGG97mlayC4yUAkhrgVypZR/EkL8CtgtpXy2zTE24F3gDycNt3SoqKhIFhcXd/j6yr1V3Pv+LvZUNuI3ZKfZDm0WDQHMKszm7q+PYdzA8Cv8+AIGe442su1wPVvK66lv8WHRBbmpdibnZzJhUDr5WU41Nq4oJ/l09zEe/GQfn5fXY7dqePwGUkqsuoYQoAnBNbOGcuPcQrKSI18bsb2intue2cixBk9YGUodVo2sZBsPXzWNyfkZEbcnloQQG6SURe2+FkIwTwNWAB8D5wFXAN+WUv7ipGNuBX4HbGn9X3+XUr7Q0Tk7CuYNbh+/fH0b728/2u1fkgDsVo0b5xTyg7NHqo07Sp/l8gbYfbSRBpcPgDSnldF5qTht5vZEa5q9/OjFzaw5UIOri3oBNouG3aLxx0sn8vXTBoR9zX9+doD/+/cuPD6DSBOhOqwadywcwR1ntremIzFFFMxbT5AJnAMsl1JWRtqg9oL5oeoWLl28inqXL6Lybk6rzrB+yTy3aFbCJY9XlGgpq2nhyVWlvLvtCMcaPDisOiceIKUM5vTPTbNz/mkDuG52AflZSRFdr7LezcUPf0Z1k+crwxqdcVo17jp7FDfPH97taz74yV4e+nS/qfUCnFada08fyk/PH2vaOaMp4mButrbBvLy2hQsfWEm9y4cZ9SOsumBodjKv3TY75ulvFSWWKupc3P3KVtYdqMGQssvAatWDk4Mzh2Xxf9+ayMCM7qe48PgDnHv/cspqXQTCeMM6rRp/+vZkvjEx9B76i8Vl/PKNbbh95q9OcVp1fnzuaL43Z5jp5zZbZ8E87mMRHn+AKx9da1ogh+AEyKGaFm5/ZiPx+LBSlGiTUvL8ukOcdd8yVu2vxuM3Quoh+wISj99g1f5qzr5vGc+vO9Tt98ifP9jD0QZ3WIEcwOUzuPuVrVQ1eUI6vry2hf95Y3tUAnmwPQH++P4u9h1risr5YyXuwfy+D/ZwvNFjWiA/wes3WF9ay5ubK8w9saLEmZSSX721nV+9tQOXNxBWUPUbkhZvgF+9tYNfv70j5IBe3eThyVWluCIMrB5/gIc/3dflcVJKvv/cppCXDYfL6ze4/ZmNYX9AJYK4BvN9x5p4cnVp1GpmunwBfv76Npo8/pC/52B1M3/+YDd3Pb+Ju1/ZymubynGrmp5KAvn9e7t4YX25Ke8bly/A8+vK+MO/d4V0/AvryzBjLZcvIHlhfRkef+f3sGJvFbsqG6MeZA0JZbXB5Ys9VVwXSj+6ogRflD9xDSl5dUM5184u6PS4yno3339uE1vK6wictBzy7S0V/OK1bdxx5ghumT9cLUtU4mr5nuP8a/VBUztALl+AJ1cd5IwR/Zg7MqfTY/+9vRK3SbsqhRBsr2hg6pDMDo9Zsmw/LV2slDFLizfA4mX7OX9C+Ktt4iluPfMmj583Nh8myrE8+Atavr/Tx8gj9S6+8bcVbDhUi8dvnLKuvdkboNkb4G8f7+N/3twe3cYqSica3T5+8PzmqDzJunwBfvDcZhrdvk6P23vUvHFlX8Bg++H6Dl8/Uu+i+GCtadcLxZ7KRvYf75lj53EL5qv3V2PRYnP5mmYvh2paOnz95qc2UOfydfoo5/IFeGlDOR/uOBqNJipKlx5ZXkKLN/Qhw+5q9vp5ZHlJh68HDGnqB4nHb1Dd7O3w9TUl1ViikK66UyIYm3qiuAXzLWV1Uf3DPJlF0/i8gx7ArsoG9hwLbUzO5Q3wUAiTNopiNl/A4MlVpRHtweiKx2/w1OqD+Dt4XDY7pYkmQO9k2HLjwTqaPbGdr3L7DNYdqInpNc0St2C+vrTG9BUsHWn2+tlSVtfua8+sOdStzGpby+v4YHulWvKoxNTS3ccJxOBvzm8YLN3dflZTIQQ5KXbTruW06RT0S+7w9Q0xHmI5YXMHsSLRxS2Yd/Z4ZTYp4VjjV9e0un0BPt55tFsfKoaEO57dxDf+trLHr0tVeo5V+6toiUEvtcUbYFUnwwyT8tNNu1bAkEwc3PH56lpiFyNO1tW8QaKKWzCPdc/W32ZDxbbD9Zz556VUNri7fS5vwGBXZQMXPLCCvy/tfHJVUcyw/kBNxLlIQiFl8Km5I1dMH0KySTleBmU4GZrdcc88Fk8iiXTdSMUtmMc6fWx60pfb+lftr+Lbi1dTUecOe6jHkMHxtb99vJf/9/JWjB682UBJfAc7mcA3/VrVzR2+tnBMLkn2yN+7TpvOHWd2WBoBALslPilqbT00SV/cWj2pk8crszmtOhMGBa+3uayO7/2z2LRZeZcvwNtbj/C/b6lli0r3NHv8rC2p5pNdRykurel0A02092OcrLPdlrom+Mvlk3FYww8duiYYnZfKRZM6r5k7PKfjXns0DetkHD+RxW3T0NShmby26fApSeWjRddgwqB0Wrx+bnrSvEB+gssX4KXichaMzuHMMarykNK50qpmFi/bz+ubD2PVNBDB4Q2AK2cM4ca5w8hNO7XYSmerPszW1ZLhM0b046qZQ3l27aHup6oWkGK38OCVU9C6WB4zszCblfuqupWVMVKagJnDsmN2PTPFrWc+qzC706ITZpISRuWl8pu3d0ZtcsPlC/DDF7ZQ3xL/yRNfwAgW2CirU6kIEsyqfVWc/7cVvLShDLfPoNHjp9Htp8kT/Ocfqw5wzv3L2VHRcMr39Us1bxVJV/qldF1A4hffGMt3ZuR3q4dut2hkJ9t49bbZDM7sOgXv5PwMbJbYhqgkm86UIRkxvaZZ4hbMB2Y4O93GaxarLvjOjCFU1Ll4dWO5aVuR2+Py+nl4afzWoUspWbJsP9N+8yGXP7Kaqx5by9TffMgf3tvV4dphJXZ2VDTwvSeLafEGOtz57AtI6l0+rnh0NRV1ri/+/5T86L9XTgjlfSmE4JcXjmfJNUVkJdtItnc8vm3VBXaLxjcmDuDTHy9geE5KSO2YXpAVh/FrwZyR/WJ8TXPEdaT/5vmFJJlc/aQtTQium13AP1aVRn2S0huQPLP2UJfJg6Ll9+/t4i8f7aXB7afZE6DJ46fFG+Cfqw7wgxc2x6VNypd++86OkIclWjwBHvzky47BrMIsnDGoWZlk05kxLCvk4+ePymHtz87ij5dO4ozh2aQ7reiawKoLLJqgsF8y180u4KP/nM99l03uVn0BXRN894xh2GPUOw92/PLjNvEaqbgm2po/KoeJg9MpLq2NypCL06pzxYx88tIcvFRchi8GwzoSyYc7jnLBxIFRv9bJjja4+eeq0nY3QLl8Bp/sPMq2w/WcNih2E8/KlyrqupdnxG9IXtt0mF9cMJYkm4Wvn9Y/JrmBAobkvG6WdbPqGudPGPBFgqoWrx+/IUmy6hEXcr5y5hAWL9sf0TlCpWuC689I/AIVHYlrz1wIwV8unxK1cbHMJCt3f30Me442Eqv5o2ZPgOV72t9BF01vbz3SaWpSj9/glQ3lMWuPcqqPdx7tdupYXROsKQlu4MlIsvG1cXmmb6k/mSbg3PH9T1nGG44km4U0hzXiQA7QL8XOf39jXNSfSpw2nf88ZxSDwqi8lCjivqCyf7qDB6+cEtFSp/Yk23Qev346DqvO54frY7oOPB7bkOtavJ3m7TAkHA+xsotivtqW7te2lVJSd9KE+g/OHhXVCUGbReP7ZyVeceMrZuQzfmAalih9kumaoCA7ie/NKYzK+WMl7sEc4Mwxedx32SRTAroQwVnz62cXUFrVzMHqZrYdro+4Mkp3dJahMVpG5KZ0ujPPYdEYNyAthi1KfPUuH69sKOdfq0ujnpohyaZ3OwOgEOKUHumI3BRuXzgiKr1Up03njoUjGJEb2uRkLAkhWHLNNHJT7egmB3RNBJ/g/3H9DNPPHWtxHTM/2fkTBpKb6uC2ZzbS4PKFvepEEAzoT3xWiq6J1kITsV3J4QtIpJQxLWRx7vj+/Py1bR2+LoHLpufHrD2J7p2tFfzopS1oIvg3IoCvje/P/ZdPjsqbuqggC4um4QuEPjnuDRhMbrNM7tb5w/lw+1F2VTaaVkrNZtEYmZvCLfOHm3K+aMhOsfPa7WdwycOrONboNmXtuVUTZCbbeOXW2fRPd3T9DQkuIXrmJxQVZLHsJwu5bHo+douGM4ye+olt9i5fcDWHyxeI6aYDCH6YxLoikcOqs/jqaTit+inBSIjgRPD/XTKBfiZmvOvJympa+NFLW3D7DFq8ATx+A7ff4MMdlTy+suN83pGYNDi92wFj1rAsBqSfOoZr0TWevmkmQ7OTsJmQ69tu0RialcTTN840ZYw7mvLSHLzz/TksHJ0b8dOJ06pz+vBs3v3BXPKzul7z3hOIeCSJKioqksXFxZ0eU9/i46UNZTy95iDltS4crb88iUQgMGSwIK2AmCQg6o4Uu4Vtvzo34vPUu3y8VFzGi8VlNLr99E93cP3sAs47bUCHY6f7jjWyeFnJF9kgZxVmcfvCEUwcnBFxe3qLP72/iyXLS9r9kB+Q7mD1T8+KynXf+/wI//ni5pCG/BxWjWdunMW0oe2v+W7y+Lnt6Q2sL60Ne0ez06YzvSCTh6+aRooJ+VZi6f3tldz98lZ8AaNbu8iTbTqaENxz8Wn8x+SBPa4MpBBig5SyqN3XEjWYn8ztC7C7spGKOhfegIFV1/jLh3s4UNVEDIfCQzZtaCav3Do7onNsLqvjmsfX4g+cWt0l2aaTnWLnpVtOJy+t5z8axsNdz2/i9c0V7b7msGrs+s15Ubv2Xz7cw5Ll+zsN6A6Lxq8vPo3LijofFpNS8ubmCn7++rYvOjehSGoNaL/toQHtBK/f4IMdlSxetp/dlY04LDpu/6lP4hZN4LTpeHwBhvVL5tYFIzhvQv8eu5a8s2DeIz6OHVadSfkZTMrPAIJFXstqXQkZyIO5HULfdNGeYw1urn5sLU2er1ZiavYGcNe5+M6ja/joh/O7zG+hfNX0YVl8sP0oLe30aEflpUb12nedM4qCfsn84d+7qHf5vgjAmghmCRyY4eCXF45n/qjOCytDcCjvoimDOGtcHq9tLGfJ8hKqGj1YdI0Wr/+LjKCaCC4X9AcM+qXauXleId+cOrjH9cbbslk0Lpg4kAsmDqSuxcu2ww1sLa/jQHUzbp+Bw6IxJDuJiYMzmDAonazkrtMU9GQ97rfZ5PFz/0d7cCdiJCf4hjx3fP+IzvHk6tJOJ7cChuRovZtle4+zcHRuRNfqiy6ePIj7P9yD2x84JQWyw6rx/84dE/3rTxnERZMHsrqkms/2VVPv8pKVZOPscXlhDYel2C1cc3oBV88aSkW9m8/L69l2uP6LAjDZyTZOG5TOhMHpDEx39NieeGcykmzMGdmvx27FN0OPC+avbSxHS+A/xoEZji+eIML1wvryLkvZNXsDPLvmkArmYUi2W3jttjP48Utb2HioFk0EVzX86sJxMQsGQghmD+/H7OHmXU8IwaAMJ4MynHz9tMg6FErP0+OC+VtbjoQ8NhhrTpvOnWdGvuki1MyORxu7XyVJCcrPSuKFm0+nvsWH2x8gN9XeK3usSt/R44L5zsqGrg+KA10TjO2fykWTI8/Jkuqw4Gnquv5hbgzTovZW6UlW0ols+7qiJILEXljahpSSRvdXJwUTgd2i8bfvTDGld3dZUX6X27aTbTpXzhwS8bUURekdelQwB7qdrCgWHFaNR64pCinhfiium13QaR5nXRPkpjmYP0qNlyuKEtSjgrkQIqGWF2mtuysfuabI1ImzvDQHT31vBil2y1fy1STbdAakO3juplk9PpeEoijm6XFj5qcNSmeZSSlmrZoAQVjb/Z1WnWH9knnoqqlRKQA7dUgmK+9eyIvFZbywvnUHaJqD784J7gB1xKBQgaIoPUdIwVwI8TgwFnhXSnlPuMeY4VvTBlNcWmNKIWirReOpG2Zw7/u72VJWhyFll4E92abjtOncvnAE155eENXecUaSjUXzhrNoXuImQFIUJTF0GcyFEJcAupRythDiYSHESCnl3u4eY5avj+/PL177POLz2C0al0/Pp6ggixdvPp1D1S08t+4QK/YdZ+/RYDpUXRNIGSyQ3D/dweT8DC6dNph5I3PUzktFURJKKD3zBcCLrV9/AswB2gbqLo8RQiwCFgEMGRL+KgybReP3l0zkxy9tCTvBEATzU/zwnFFf/PeQ7CTuPm8MdzMGw5BUNXlw+QJY9GBFcTWsoShKIgtlAjQZONz6dQOQF84xUspHpJRFUsqinJyu80505hsTB3Dm2Nywi1k4rBoPXjmVtA6Ky2qtq0WGZiczKMOpArmiKAkvlGjYBJxIqpzSwfeEcoyp/nL5ZOaM6NetvMaCYCC//7LJnDGi7+ZwUBSl9wkl6G4gOGwCMAkoDfMYU1l1jUevLeJn54/FadWxd7HJJsmmU5iTzBu3z+G8Cd2rPq4oipLoQhkzfx1YIYQYCJwHXCGEuEdK+YtOjplldkPbI4TgmtOHct6E/jy/7hDPrD3E8UYPDquOEGAYwdUpEwens2heIWeOyU34aiqKoijhCKk4hRAiEzgHWC6lrAz3mJOOPQ4cbOelfkBVlw3qvfry/fflewd1/335/rtz70OllO1OOsal0lBHhBDFHVXR6Av68v335XsHdf99+f7Nunc15qAoitILqGCuKIrSCyRaMH8k3g2Is758/3353kHdf1++f1PuPaHGzBVFUZTwJFrPXFEURQmDCuaKoii9QFyCuRDicSHEKiHELyI5pifq6r6EEOlCiPeEEB8KIV4TQiRONQ4ThPp7FULkCSE2xapdsdKN+39YCHFhrNoVCyH87WcKId4VQqwQQiyOdftiofXvekUnr1uFEG+3/pxu6M65Yx7MT06XCwwUQnylnH0ox/REId7XVcB9UspzgErg67FsYzR18/f6J77M99MrhHr/Qoi5QH8p5VsxbWAUhXjv1wBPSynnAqlCiF617rx1Y+WTBBMTduROoLj153SBECI11PPHo2e+gK+myw3nmJ5oAV3cl5TyYSnlh63/mQMci03TYmIBIfxehRBnAs0EP8x6kwV0cf9CCCvwKFAqhLgodk2LugV0/buvBkYLITKAfOBQTFoWOwHgcoKZZTuygC9/TquAkD/Q4hHMTUmp20OFfF9CiNOBTCnlmlg0LEa6vP/WYaVfAv8Vw3bFSii//2uBHcC9wAwhxJ0xalu0hXLvK4GRwPeBXUBtbJoWG1LKBillfReHhR374hHMEzKlboyEdF9CiCzgAaBbY2Y9QCj3/1/AQ1LKulg1KoZCuf8pwCOt+Y2eBhbGqG3RFsq9/w64RUr5a4LB/LsxalsiCTv2xSNIJmRK3Rjp8r5ae6YvAj+VUraXjKwnC+X3ejZwuxBiKTBZCPFYbJoWE6Hc/z6gsPXrItpPSNcThXLvScAEIYQOzAT64iaY8GOflDKm/wBpwBbgPmBna4Pv6eKY9Fi3M473fivBx8ulrf9cHu92x/L+2xy/NN5tjsPvPxV4CVgOrAYGxbvdMbz3GcB2gr3TD4GUeLc7Sj+Lpa3/PhO4o81rQ1t/Bn8F1hOcNA7pvHHZAWp2St2epLfeV6jU/ffd++/L994drXUh5gDvy67H2L/8vngEc0VRFMVcvWViUVEUpU9TwVxRFKUXUMFcURSlF1DBXFEUpRdQwVxRFKUX+P+ZyNov05+d/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num = 50\n",
    "x = np.random.rand(num)\n",
    "y = np.random.rand(num)\n",
    "area = (30 * np.random.rand(num)) **2\n",
    "plt.scatter(x, y, s=area)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.7.2　实例7：汽车速度与制动距离的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 07_vehicle_speed_and_braking_distance\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "# 准备 x 轴和 y 轴的数据\n",
    "x_speed = np.arange(10, 210, 10)\n",
    "y_distance = np.array([0.5, 2.0, 4.4, 7.9, 12.3, \n",
    "                       17.7, 24.1, 31.5, 39.9, 49.2,\n",
    "                       59.5, 70.8, 83.1, 96.4, 110.7,\n",
    "                       126.0, 142.2, 159.4, 177.6, 196.8])\n",
    "# 绘制散点图\n",
    "plt.scatter(x_speed, y_distance, s=50, alpha=0.9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.8　绘制箱形图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.8.1　使用boxplot()绘制箱形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "data = np.random.randn(100)\n",
    "# 绘制箱形图:显示中位数的线条、箱体宽度为0.3、填充箱体颜色、不显示异常值\n",
    "plt.boxplot(data, meanline=True, widths=0.3, patch_artist=True,  \n",
    "            showfliers=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.8.2　实例8：2017年和2018年全国发电量统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 08_generation_capacity\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.family'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "data_2018 = np.array([5200, 5254.5, 5283.4, 5107.8, 5443.3, 5550.6, \n",
    "                      6400.2, 6404.9, 5483.1, 5330.2, 5543, 6199.9])\n",
    "data_2017 = np.array([4605.2, 4710.3, 5168.9, 4767.2, 4947, 5203, \n",
    "                      6047.4, 5945.5, 5219.6, 5038.1, 5196.3, 5698.6])\n",
    "# 绘制箱形图\n",
    "plt.boxplot([data_2018, data_2017], labels=('2018年', '2017年'),\n",
    "            meanline=True, widths=0.5, vert=False, patch_artist=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.9　绘制雷达图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.9.2　实例9：霍兰德职业兴趣测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 09_holland_professional_interest_test\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.family'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "dim_num = 6\n",
    "data = np.array([[0.40, 0.32, 0.35, 0.30, 0.30, 0.88],\n",
    "                 [0.85, 0.35, 0.30, 0.40, 0.40, 0.30],\n",
    "                 [0.43, 0.89, 0.30, 0.28, 0.22, 0.30],\n",
    "                 [0.30, 0.25, 0.48, 0.85, 0.45, 0.40],\n",
    "                 [0.20, 0.38, 0.87, 0.45, 0.32, 0.28],\n",
    "                 [0.34, 0.31, 0.38, 0.40, 0.92, 0.28]])\n",
    "angles = np.linspace(0, 2 * np.pi, dim_num, endpoint=False)\n",
    "angles = np.concatenate((angles, [angles[0]]))\n",
    "data = np.concatenate((data, [data[0]]))\n",
    "# 维度标签\n",
    "radar_labels = ['研究型(I)', '艺术型(A)', '社会型(S)', \n",
    "                '企业型(E)', '传统型(C)', '现实型(R)']\n",
    "radar_labels = np.concatenate((radar_labels, [radar_labels[0]]))\n",
    "# 绘制雷达图\n",
    "plt.polar(angles, data)\n",
    "# 设置极坐标的标签\n",
    "plt.thetagrids(angles * 180/np.pi, labels=radar_labels)\n",
    "# 填充多边形\n",
    "plt.fill(angles, data, alpha=0.25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.10　绘制误差棒图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.10.1　使用errorbar()绘制误差棒图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "x = np.arange(5)\n",
    "y = (25, 32, 34, 20, 25)\n",
    "y_offset = (3, 5, 2, 3, 3)\n",
    "plt.errorbar(x, y, yerr=y_offset, capsize=3, capthick=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.10.2　实例10：4个树种不同季节的细根生物量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 10_fine_root_biomass\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.family'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "# 准备 x 轴和 y 轴的数据\n",
    "x = np.arange(3)\n",
    "y1 = np.array([2.04, 1.57, 1.63])\n",
    "y2 = np.array([1.69, 1.61, 1.64])\n",
    "y3 = np.array([4.65, 4.99, 4.94])\n",
    "y4 = np.array([3.39, 2.33, 4.10])\n",
    "# 指定测量偏差\n",
    "error1 = [0.16, 0.08, 0.10]\n",
    "error2 = [0.27, 0.14, 0.14]\n",
    "error3 = [0.34, 0.32, 0.29]\n",
    "error4 = [0.23, 0.23, 0.39]\n",
    "bar_width = 0.2\n",
    "# 绘制柱形图\n",
    "plt.bar(x, y1, bar_width)\n",
    "plt.bar(x + bar_width, y2, bar_width, align=\"center\", \n",
    "        tick_label=[\"春季\", \"夏季\", \"秋季\"])\n",
    "plt.bar(x + 2*bar_width, y3, bar_width)\n",
    "plt.bar(x + 3*bar_width, y4, bar_width)\n",
    "# 绘制误差棒 : 横杆大小为 3,  线条宽度为 3,  线条颜色为黑色, 数据点标记为像素点\n",
    "plt.errorbar(x, y1, yerr=error1, capsize=3, elinewidth=2, fmt=' k,')\n",
    "plt.errorbar(x + bar_width, y2, yerr=error2, capsize=3, elinewidth=2, fmt='k,')\n",
    "plt.errorbar(x + 2*bar_width, y3, yerr=error3, capsize=3, elinewidth=2, fmt='k,')\n",
    "plt.errorbar(x + 3*bar_width, y4, yerr=error4, capsize=3, elinewidth=2, fmt='k,')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
